ML Decision Tree for Vehicular Search Path Selection
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Solution Overview
Problem
The existing vehicle search process on the internet is time-consuming and inefficient, with users often encountering redundant information and needing to backtrack during their search for a vehicle.
Innovation Solution
A computing model trained using machine learning techniques processes user queries by generating a decision tree based on historical data, allowing it to select relevant search phases and paths, thereby providing targeted search results and improving the efficiency of the search process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional vehicle search processes are used, then users can access comprehensive vehicle information, but the search process becomes time-consuming with significant backtracking and dead ends
Solution Approach 1:
The system performs preliminary actions by training a machine learning model on historical search data beforehand. The model learns optimal search paths and user preferences in advance, enabling it to predict and guide future search processes efficiently without requiring users to experience trial and error during actual vehicle searches.
Solution Approach 2:
The patent replaces the manual, mechanical search process with an automated machine learning-based recommendation system. Instead of users manually navigating through search results and filters, the ML model automatically generates personalized search recommendations, substituting human cognitive effort with computational intelligence.
2Loss of information
If comprehensive vehicle information is presented to users, then users have sufficient information to make decisions, but redundant and unnecessary information is also presented
Solution Approach 1:
The system applies local quality by customizing information presentation based on individual user characteristics and preferences. The ML model analyzes historical data to determine which specific vehicle attributes and information types are most relevant to each user, thereby presenting information with varying levels of detail and focus tailored to local user needs rather than applying a uniform information structure to all users.
Solution Approach 2:
The patent uses partial action by selectively presenting only the most relevant vehicle information to each user based on ML predictions. Instead of providing all possible vehicle information equally, the system identifies and presents the subset of information most likely to be useful to the specific user, avoiding the excessive presentation of redundant data while maintaining sufficient information for decision-making.
3Reliability
If traditional search tools are used, then users can gather sufficient information about vehicles, but the process requires significant user effort and backtracking
Solution Approach 1:
The patent introduces a machine learning recommendation system as an intermediary between users and vehicle search data. This intermediary layer processes user queries and historical data to generate personalized search recommendations, mediating the interaction between users and the complex vehicle database. The intermediary simplifies the user interface while maintaining access to comprehensive information, thereby improving reliability without exposing users to system complexity.
4Productivity
If users manually navigate through search results, then users can control their search process, but the process becomes inefficient with dead ends and redundant information
Solution Approach 1:
The system implements dynamics by making search recommendations adaptive and flexible rather than static and rigid. The ML model dynamically adjusts search recommendations based on user responses, preferences, and behavior patterns learned from historical data. This allows the system to adapt to changing user needs while maintaining efficient search paths, balancing productivity improvements with preserved user control and versatility.
Data Source
AI summary
Systems, methods, and computer readable media for vehicular search recommendations using machine learning. A computing model may receive a query for a vehicular recommendation. The model may be trained based on historical queries, webpages visited, and attributes of vehicles. The model may generate a decision tree comprising a plurality of paths for processing the query, the plurality of paths comprising a subset of a plurality of available paths for processing the query, each path associated with a plurality of search phases. The model may select, based on the decision tree, a first path of the plurality of paths for processing the query. The model may select, based on the first path, a first search phase of the plurality of search phases as corresponding to the query. The model may then return a search result corresponding to the first search phase as responsive to the query.